Today, the concept of centralized laboratory services is well accepted, but that was not always the case. Two decades ago, the central laboratory industry was born out of a need for a more accurate way to collect and report clinical trials data. Since that time, the industry has grown into a billion-dollar market that is now a critical component in more than 70% of all clinical trials worldwide. 

This year marks the 20th anniversary of the central laboratory industry. Central laboratories analyze blood, tissue, urine, and other specimens through biochemical assays, providing nearly 90% of all data that comprise a new drug application. As biopharmaceutical companies are under continued pressure to provide enhanced therapies that help improve and lengthen human life, reduce drug development costs, and speed drug development cycle times, the central laboratory industry is becoming more important than ever. 			

As the biopharmaceutical industry has been maturing in many established markets, the demand for improved drug therapies in emerging markets has been exploding, increasing the opportunity for the industry to reach treatment naive patients and provide an opportunity for continued market growth. The areas that have seen the largest growth in emerging markets include Eastern Europe, Latin America, India, and Asia Pacific.

The central laboratory services industry is well positioned to support the growth in clinical trials within these new markets as signified by its growth and investment in these regions of the world. For example, three of the leading central laboratory services companies have dedicated central laboratory facilities in Asia Pacific and there are projections this focused expansion will continue. 			

Conducting clinical trials represents an average of 37% of the total cost to bring a new medicine to market, and laboratory testing represents a critical step in supporting the evaluation of a new product.  The benefits of having centralized information and the value of consistent data reporting that is associated with a central laboratory is increasingly gaining favor with the regulatory community and drug sponsors due to the significant cost and importance of this data to a regulatory submission. 

With R&D spending expected to reach $140 billion by 2009, the central laboratory industry is poised for continued growth. Generating $1.8 billion dollars in 2006, the industry is expected to grow an average of 12% to 13% a year over the next four years, reaching $2.9 billion by 2009.

Taking a look back at the 20-year history of this industry, it is interesting to see how an idea evolved into a billion-dollar market in just 20 years, and what the future holds for the central laboratory services industry. 

Historically, when clinical trials first began, the laboratory results obtained were used primarily to evaluate safety rather than efficacy. Gauging a drug's ability to treat diseases and symptoms was more observational than biochemical, so sponsors could use local laboratories for testing. As biochemical efficacy data began to be used by the industry for major statistical endpoints, higher capacity, faster, and more homogenous lab facilities were needed. 			

In the 1980s, when a clinical trial commenced and laboratory test data was returned, pharmaceutical companies would typically receive data with a 39% error rate. Human error accounted for the majority of data inaccuracies, which were caused by such things as mislabeled test kits, incorrect tests, and missing specimens.

Prior to the advent of the first central laboratory in 1986, testing for clinical trials was managed by clinical trial sponsors through the use of many local or regional laboratories—including hospitals, clinical laboratories, and university research departments—which often increased the possibility for data errors, lengthened study timelines, and increased study costs. 

SciCor employees gather around the first specimen collection kit circa 1986.			

During this time, the analysis of clinical trials data often required months of work, with the data having to be "cleaned" first before it could be analyzed. Burdened by the difficulties associated with gathering data from multiple local laboratories that used different testing methodologies, reference ranges, and standard operation procedures, the idea for a central laboratory was conceived. 

The four founders of what eventually became the world's first central laboratory were committed to devising a method that would provide globally combinable, high-quality laboratory data to study sponsors to speed up clinical trials and reduce the time spent on data cleaning. The goal was to establish a business that could collect specimens from investigator sites and deliver them to a central laboratory within 48 hours, while providing study sponsors with a less than 2% error rate. 

The world's first central laboratory, located in Indianapolis, Indiana, was founded in 1986 under the name SciCor, which later became Covance Central Laboratory Services. At that time, SciCor was the only company that provided data within 48 hours using the same clinical trial methodology with less than a 2% error rate. Twenty years later, the business plan built around SciCor has been the genesis of an entire central laboratory services industry. 

By April 1986, the first study was underway. This study processed clinical trial laboratory specimens from across North America with a staff of only nine individuals, who were committed to providing test results within 48 hours. Reaching this goal, however, did not come without its challenges. 

In order to deliver to study sponsors faster access to higher quality combinable data, it was necessary for the first central laboratory to achieve faster receipt, analysis, and a significantly reduced rate of error. Through the establishment of relationships with global logistics couriers, standardization in the production of study kits, and the development of a single global database, the central laboratory industry has been able to deliver combinable data to clinical trial sponsors. 

The notion of "combinability" was essential if biological data was to be received from different laboratories but stored and transferred into a single global database. For combinable data, the key was using the same testing methods, reference intervals for each assay, and standard operating procedures across each central laboratory location. 

In order to deliver combinable data, a central laboratory must use the same methodology principles, calibrators, and  reagents. To provide combinable data to study sponsors, the central laboratory also has to provide a way to consistently collect biological specimens. SciCor was a pioneer in the design and development of the first protocol visit specific laboratory specimen collection kit. The specimen collection kit was originally developed to reduce the error rate at investigator sites that resulted from not having the appropriate tests or test materials at the time of a patient visit. 

These specially prepackaged specimen collection kits allowed sites to collect and ship samples to the central laboratory in a homogenous way and provided for improved tracking of specimens within kits, as each kit and its components were barcoded for improved traceability. Missing tests could be identified faster and easier, enabling earlier identification of missing data points and providing the ability to remedy these errors prior to final data analysis. 

Today's specimen collection kits can be quickly shipped, scanned into a central database, and sorted by a highly automated process. Once sorted, the specimen collection kits are then transferred to the appropriate investigator sites for testing. 

Another important component needed to improve data delivery and support enhanced combinability was accelerated global central laboratory specimen shipment and receipt timelines. One of the primary goals of the central laboratory was to achieve a 48-hour turnaround time on the shipment of laboratory specimens from investigator sites to the central laboratory testing locations, but the growth of clinical trials around the world made this a challenge. 

SciCor was the first central laboratory to establish dedicated relationships with logistics couriers in order to deliver faster specimen shipment timelines. In addition, they developed special packaging for specimens to enhance specimen visibility and identification during shipment. Today, clinical trial sponsors expect a central laboratory to provide efficient, high-quality specimen shipping, receiving, and tracking services, and they evaluate service providers based on their logistics consulting and contingency planning capabilities. 

Many central laboratories outsource their courier services to commercial worldwide shipping organizations that specialize in clinical trial specimen logistics, developing numerous strategic partnerships with global couriers. Logistics entails detailed coordination with study sponsors, investigator sites, couriers, regulatory bodies, and their own scientific and technical personnel to get samples from study subject to laboratory and submitted in time for sponsor review. 

How a central laboratory handles its transportation system can have a dramatic effect on how well they support sponsors needs. For example, on 9/11 air space was closed down in the United States. Due to the fact that a contingency plan was in place, Covance was able to use ground couriers in North America to deliver specimens to its central laboratory, rerouting specimens from Latin America to its central laboratory in Geneva, Switzerland. In total, the company was able to contact more than 22,000 investigators—every single client contact in less than three hours. 

As the benefits of the central laboratory model became widely accepted, the use of the model was expanded to other regions of the world. The benefits of using one central laboratory, rather than small, localized laboratory facilities or multiple specialty laboratories are based on the need for homogenous data integration from disparate laboratories to improve submission quality and reduce the timelines for data submissions. Central laboratories accomplish this by combining the use of high throughput technology with efficient internal systems that make for quick and more combinable collection of lab data. 

The 1990s brought an increase in pharmacological study complexity, globalization of clinical trials, and a greater urgency by biopharmaceutical companies to bring their drugs to market faster. By spring 2006, the only pharmaceutical company to maintain an internal central laboratory facility decided to close its operation and outsource—making the central laboratory industry a 100% outsourced market. 

. Due to an increasing trend in the focus of drug research and development on more complex conditions, safety concerns, requirements for approved drugs, and increasing pressures on launched drugs, the biopharmaceutical industry is searching for ways to more quickly identify products with greater possibility for market. The use of biomarkers to support candidate selection and evaluation in clinical development is a growing trend for the central laboratory business. 

Central laboratories today offer a variety of biomarker testing services in addition to routine testing such as chemistry, hematology, and urine analysis. Working closely with individual clients to customize the needs of biopharmaceutical drug development, central laboratories conduct thousands of distinct biomarker testing assays. 

Technology and the Virtual Central Laboratory

. In order to provide faster data analysis and reporting, biopharmaceutical sponsors are seeking new, innovative ways to capture, store, and access clinical trial data during a study. New laboratory technology is responding to sponsors' needs. 

As the growth in complex testing areas increased, such as oncology and critical care, the need for improved methods of collecting higher quality local laboratory data expanded. In some clinical trial situations, a local or regional laboratory solution may be preferred, but this raises questions as to the quality and consistency of the results. In 1995, Dr. Jaap Dijkman and Dr R.T.P. Janssen developed a unique solution: the Virtual Central Laboratory. 

 , now a division of Covance Central Laboratories, was designed for clinical trial studies that require fast turnaround time, such as acute care and oncology studies or studies in regions where transport logistics or other reasons preclude the use of a traditional central lab. Using a unique calibration processes and technology and a network of local laboratories, the Virtual Central Laboratory

  improves data collection and access to laboratory test results from a single global database and has supported product submission approvals in the United States and Europe. 

The central laboratory services market began with a focus on centralizing the global collection of laboratory data, but the increasing complexity of clinical trial laboratory data is pushing the industry in new directions. 

The future of the central laboratory may evolve into two different scenarios: One possible scenario is a pure central laboratory model in which industry players expand their central  laboratory facilities into new geographies in order to better service local laboratory studies.  

The second scenario would include a hybrid laboratory model in which central laboratories and virtual central laboratories co-exist to service both central and local laboratory clinical trial needs. The barrier to expansion of the pure central laboratory is financial, while the challenge with a hybrid central laboratory model will be delivering data consistency and combinability.  

Evolution within the central laboratory industry is inevitable. The many breakthroughs that have come to life over the past 20 years leads us to reflect upon and consider the original drivers behind the central laboratory model.  

As we move into the future, the geographic expansion of clinical trials, development of emerging markets, technological advancements in biomarker testing and improvements in ancillary laboratory services will impact how fast and in what direction the industry will grow. It will be interesting to see exactly what the next 20 years will hold. 

Articles

Electronic Data Capture

A detailed history of the billion-dollar industry since its inception in 1986 and what the future may hold.

The Central Laboratory: 20 Years of Evolution

Pharmaceutical companies and clinical research organizations have been working to improve productivity and return on investment by modifying processes, decreasing cycle times, and implementing improvements wherever possible.

  According to the FDA, it is during the clinical trial process that 9 out of 10 experimental drugs fail.

  Recent research by Joseph DiMasi of the Tufts Center for the Study of Drug Development suggests that reducing the length of clinical trials by 25% would save $129 million per drug. 

Phase I studies are the starting point for drug trials in humans and are a vital stage of clinical development, where critical decisions are made with regard to whether an investigational drug proceeds into Phase II efficacy trials or is halted. Rapid, scientifically based decisions not to proceed can achieve cost savings and redirect financial resources to more promising drug candidates. Numerous technological innovations have recently begun to automate Phase I trials, but one critical component remains virtually untouched: effectively integrating clinical lab data directly into studies to improve both safety and speed. 			

Currently, most Phase I clinical research centers receive clinical lab results on paper, which requires intensive and time-consuming review by staff and opens test interpretation to human error. Many of these test results are used to assure subject safety, so timely review is extremely important. In addition, sponsors often request electronic data transfer of clinical results at the end of the study. Preparing electronic data from paper reports is frequently a cumbersome, time-consuming, error-prone process. 

This article discusses why standard clinical lab systems are generally not well suited to the process of Phase I clinical trials, and why an electronic system that is tightly integrated with the operations of a Phase I clinic is essential for process improvement and, most importantly, timely evaluation of results. 

Test results from clinical labs are used extensively in Phase I clinical trials, primarily for screening and safety assessments, and increasingly as biomarkers. For Phase I clinics with more than 100 beds, it is often cost effective to establish an on-site clinical laboratory. These labs are likely to deploy software systems that automate laboratory processes and store test results in a database. 

Most clinical lab software tends to focus on the needs of medical facilities whose emphasis is on rapid testing for diagnostic evaluation and billing automation. These systems do not readily facilitate the timely review of test results from a subject safety or inclusion/exclusion perspective, nor do they provide capabilities for database creation for statistical analysis purposes—essential features for Phase I research. Significant advantages could be realized with a lab information system that tightly integrates with the processes of Phase I studies (see Figure 1). 

 Figure 1. An EDC system better suited for Phase I studies would capture inclusion/exclusion rules, potentially saving the PI time. In addition, automatic alerts would increase the safety of subjects.			

Features are needed that would allow test definition and sample processing based on individual study protocols. Integration of test results with automated alerting could reduce the human resources needed to process the lab data. It could also facilitate a more efficient review of the clinical lab results in the areas of qualifying subjects, evaluating safety results, preparing adverse event (AE) documentation, investigator result sign-off, and data audits. Improved study safety for subjects would be achievable with real-time alerting based on test results. This would allow for timely consideration of whether to continue dosing, escalate dosing, repeat dosing, or stop. 

Typical clinical lab software is focused on the needs of medical clinics (hospitals and physician offices), which are very different from those of Phase I clinics. For a medical clinic it is of great importance to assure that each test meets Medicaid-defined billing rules based on the patient's diagnosis. Since most Phase I facilities do not bill clinical tests based on patient diagnoses, these rules are usually unnecessary and tend to confuse the standard operations of the Phase I clinical laboratory. 

Medical clinical labs must always test a sample based on all ordered test panels. For Phase I clinical labs, a cost savings could be realized if testing for a subject was canceled based on the results of one test. For example, a confirmed positive pregnancy value or positive urine drug screen could trigger a study exclusion rule. In this case no additional testing would be required, as the subject would be excluded from the study. 

In most medical centers, lab test methods and patient alert limits are defined once for the whole facility and are rarely changed. In a Phase I clinic, lab tests, panels, test methods, and patient alert limits may change from protocol to protocol. 

Integrating information & study processes

Phase I protocols often require sample collections at the initial screening and at various time points during the initial and subsequent dosing. An integrated clinical lab system could automatically order these samples based on the study protocol so that collection and processing would be more efficient. Preprinting of barcoded collection labels would also speed collection and processing. Barcode scanning could automatically check the IDs of subjects and collection, sample, and transfer tubes—virtually preventing human error. 

Once the subjects are admitted for a Phase I study (usually 12–24 hours prior to dosing), additional samples may be collected for testing. This ensures that inclusion and exclusion criteria are met for each subject and catches recent changes that might affect the subject's study eligibility. In addition, the results may serve as baseline values against which to evaluate any postdose laboratory result variations. The Principal Investigator (PI) makes a final decision to dose based at least in part on the review of a paper-generated report. In a tightly integrated electronic system, automated alerts could notify the PI of subjects that fall outside the study limits. An electronic report with appropriate search and sort capabilities would improve the speed of subject review and approval. Just prior to dosing, an additional automated review would have the potential to improve subject safety by capturing any potentially late arriving test results. 

It is common in Phase I study protocols to schedule sample collections after the initial dose for additional clinical testing. This testing assures the safety of the subjects and satisfies the criteria for continued dosing. If test results are available electronically, automatic alerts may be generated to reveal subjects who are exceeding the clinically defined safety ranges. With automated alerts, subjects whose test results exceed safety limits can be quickly evaluated for a potential AE and treated. Ranges of test values, along with the medical judgment of the PI, determine if the AE is rated as mild, moderate or severe. An electronic system could produce reports to show all lab values related to a particular subject experiencing the AE. This could both improve subject safety and the reporting of AEs by alerting clinicians in a timelier manner. 

Further alerts could identify subjects no longer qualified for continued dosing based on protocol-defined test limits. These alerts and specially formatted reports would decrease the time needed for subject evaluation. Finally, just prior to dosing, the subject's test results could be automatically checked and flagged if out of range. This would improve safety and assure compliance with the protocol's dosing rules. 

PIs need timely and thorough documentation and assessment for each AE that occurs throughout a clinical trial. This documentation and assessment is critical in helping to identify clinically significant trends that may affect subject safety. The PI determines if the event is related to the test compound, and typically a classification of the event is recorded. Current systems usually involve a hard copy of the AE form passing through several hands, which may delay the assessment process. A tightly integrated lab information system would send electronic AE test results directly to research staff and sponsor, and would include the results in a final AE report. 

In an escalating dose study, "stopping rules" may be associated with one or more clinical laboratory test values exceeding a range defined by the protocol. These values are based on samples taken from the subjects after receiving the dose. A tightly integrated electronic system could generate automatic alerts for these values so that the PI and research staff could improve the speed with which safety assessments are made. Likewise, appropriate electronic forms with search and sorting capabilities could speed the process of evaluation when a dose escalation has been completed. In addition, the ability to generate subject-specific laboratory result and/or AE reports would aid in the evaluation of trends. 

Many Phase I clinics do not have internal clinical laboratories.  If they do, they often have pre-existing software systems that are incapable of tight integration with the clinic's other electronic systems. In order to integrate test results, it is necessary to import them using some electronic data transfer method. Most outside reference laboratories submit clinical lab results in one or more electronic formats. In many cases this is a proprietary delimited flat file format, which is commonly referred to as a CSV (Comma Separated Value) file format. More advanced formats used include HL7 (Health Language 7) and CDISC formats from the Clinical Data Inter-

All electronic transfer methods must include the coded test names, test results, and the testable and clinical action ranges associated with the test method. During the result transfer a properly functioning automated system would automatically crosscheck the testable and clinical action with the range values that were approved for the study. Alerts should be generated if the range values do not match. Alerts and appropriate audit reporting for incorrect range values have the potential to reduce approval times. 

Although importing test results electronically is by far the preferred method of integrating clinical lab results, it is not always possible to do so. Some reference laboratories do not offer test result exports in electronic format, and realistically there will always be some small volume of esoteric tests for which it would not be cost effective to create an electronic interface. For this reason there will always need to be a data entry screen for tests and their associated testable and clinical action ranges. Alerts for ranges not matching those approved for the clinic or study are necessary here as well. 

Lastly, electronic files usually do not include investigator comments for out-of-range lab tests, but this information needs to be captured in the clinical database. Having this information entered by the PI or research staff at the Phase I clinic into a tightly integrated Phase I clinical lab system would be more efficient both cost wise and timewise. 

Clearly, any effort to increase the speed of drug development must address the unique needs of Phase I clinical trial centers. Current clinical lab software systems, developed to meet the needs of hospitals and other medical facilities, fail to adequately address the needs of Phase I clinics and are not the answer. Using a Phase I-specific, study-centric, clinical electronic data system would significantly enhance the review of lab results and study execution; expedite study decision making and completion; increase the speed of determining subject entrance criteria; and improve the safety of study subjects. 

1. E. Pena, "Making Metrics Matter: The Changing Paradigm of R&D Metrics," 

, PhD, RPh, is vice president and chief scientific officer with CEDRA Clinical Research, LLC, Austin, TX. 

, PharmD, is president and chief executive officer with Advanced Biomedical Research, Inc. (ABR), Princeton, NJ. 

 is president and chief executive officer with Green Mountain Logic, Montpelier, VT, email: 

*To whom all correspondence should be addressed.

An integrated clinical lab EDC system can accelerate decision making and improve subject safety in early trials.

The Need for Electronic Data Capture in Phase I

Institut De Pathologie B35 Chu Sart-Tilman

Resources

A listing of clinical laboratories arranged alphabetically, with addresses and contact details.

Clinical Lab Directory

Over the past decade quantitative medical imaging has played an important role in an increasing proportion of clinical trials. This is particularly true in areas where both the disease and the effects of treatment manifest as structural or functional changes that can be directly imaged. Imaging is, of course, less useful in areas such as pain management or psychological illness, where the method offers little insight into biological processes, and in areas where convenient chemical biomarkers are available. 

The reasons for including a quantitative imaging component in a particular trial are varied, but in general investigators have been attracted by the potential to provide a window into the direct biological effects of a drug (either augmenting or replacing subjective radiological interpretation) as well as downstream endpoints such as subjective pain scores or clinical assessments. The motivating force behind this transition is the increasing need to demonstrate mechanism of action and to assess both safety and efficacy with small numbers of experimental subjects in the early stages of the drug development process. In order to achieve this goal, an endpoint is needed that has both tight linkage to treatment effects and low measurement variability. In many cases, quantitative imaging meets both of these requirements. 			

One important example of an area where quantitative imaging can provide vital insight is the use of anti-angiogenic or vascular disruptive agents in the treatment of solid tumors. There are numerous molecules in these classes in the development pipeline at a wide range of pharmaceutical and biotechnology companies. Experience with drugs of this type that are already in clinical use, such as Avastin, has shown that the correlation between semiquantitative assessments such as response evaluation criteria in solid tumors (RECIST)

  and patient survival is very poor for these compounds.

  This is a result of the mechanism of action of anti-angiogenic and vascular disruptive agents, which is to alter the blood supply to the interior of the tumor, thereby either directly inducing cell death or increasing access to the tumor core for a second cytotoxic agent in combination therapy. A typical early effect of treatment is then either diffuse cell loss within the tumor or necrosis and cavitation rather than outright shrinkage. Neither of these effects will be captured by a RECIST assessment. 			 

A more useful examination of treatment effect in a small trial in such a case might be to combine measurement of tumor radio-density using computerized axial tomography (CT)

  with direct measurement of tumor blood flow and vascular permeability using dynamic contrast-enhanced MRI (DCE-MRI).

  These assessments are targeted directly at the compound's mechanism of action and are sufficiently reproducible to permit confident determination of treatment effect with a relatively small sample size.   

Osteoarthritis is another area where quantitative imaging can have a major impact on study timelines, cost, and success. This disease is characterized by structural changes in the joints affecting both the cartilage and the bone. Disease progression is typically assessed using subjective pain scoring and/or measurement of joint space from plain film x-ray images. Both these measures carry high measurement variability, and joint space changes slowly enough that subjects must typically be followed for years before significant changes are seen. Quantitative MRI, however, has the capability to directly track cartilage changes such as local volume loss or the development of focal defects, as well as bone marrow edema and the development of osteophytes.

 Additionally, using a technique known as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), it is possible to track changes in the structural integrity of cartilage that occur long before the onset of joint space loss.

  Taken together, these techniques have the potential to greatly reduce both the size and duration of a clinical trial designed to assess the mechanism of action or efficacy of a disease-modifying osteoarthritis drug (DMOAD), from several hundred to a thousand or more subjects followed over two–five years to 100 or fewer subjects followed for 12–24 months.  

These examples are not meant to suggest that a Phase III trial could be carried out using these techniques with 50 or 100 subjects. However, quantitative imaging may permit the decision point on mechanism of action and efficacy to be pushed back from Phase II/III to Phase I in many cases. This makes it possible for the pharmaceutical company to intelligently allocate resources away from ineffective compounds and toward effective ones in a much more efficient way. The Tufts Center for the Study of Drug Development in its Impact Report (Volume 4, Number 5, September/October 2002) estimated that it costs $808 million, on average, to develop and win market approval for a new drug in the United States. This report stated that better preclinical screens, which would increase success rates from the current 21.5% to one in three, could reduce capitalized total cost per approved drug by $242 million. These are exactly the types of changes that are needed if drug development costs are to be brought under control, and the increasing incorporation of quantitative imaging into both early- and late-phase studies is one critical tool for bringing these changes about.     

While quantitative imaging has the potential to provide significant savings to the drug development process in terms of both time and total cost, it is important to understand that both the imaging and analysis techniques required can be complex, and the value provided will be in direct proportion to the quality of the data and analysis. Quality in the case of a clinical trial is expressed primarily in terms of the measurement coefficient of variability (CV). This is defined as the standard deviation of repeated measurements divided by their mean and represents the amount of measurement variation expected in the absence of any true biological change.   

To see why measurement CV is so important, consider the case of a cardiovascular clinical trial for a compound that is expected to reduce plaque in the carotid arteries. Measurement of plaque burden is to be accomplished using ultrasound imaging at baseline and again after six months of treatment.  The control group is expected to show a mean increase in plaque burden of  2% Â± 2%. The treatment group is expected to show a decrease in plaque burden of similar magnitude and variability. With perfect measurement (i.e., CV = 0), a statistically significant result (p < .05) can be achieved with only seven subjects per arm. Unfortunately, this is generally not achievable. If the measurements are made using a well-controlled semi-automated analysis system (CV = 4%), the number of subjects per arm required for statistical significance increases to 28 per arm—a reasonable size for a Phase I trial. If the measurements are made manually at the imaging site (CV = 16%), the number of subjects required to achieve a significant result increases to 353 per arm. The last option may well be the least expensive on a per-subject basis. However, going down this path will clearly lead either to an immensely longer and more costly trial or failure to achieve a statistically significant result.  

There are a number of points in the image acquisition and analysis chain where measurement variability can be introduced. It is vital for a trial incorporating imaging to address them all. As an example, consider an oncology trial incorporating DCE-MRI to measure treatment-induced changes in tumor vasculature. This technique involves repeated MR imaging, typically at 5- to 10-second intervals, over a period of several minutes, during which the subject is given an injection of a gadolinium-based contrast agent. Because gadolinium is a paramagnetic element, local gadolinium concentration at a particular time can be inferred from observed changes in MR signal from baseline. A linear system model of the vascular bed is then used to calculate biologically relevant parameters such as blood flow and vascular permeability. 

This analysis assumes first and foremost a spatially invariant and well-defined relationship between gadolinium concentration and signal change. However, problems with the MR coils, pulse sequence, and magnetic field homogeneity can render this assumption, and therefore the entire analysis, invalid (see Figure 1). DCE-MRI analysis is also vulnerable to subject motion during the imaging session. Respiratory motion in the chest and abdomen is particularly troublesome. Typical solutions to this problem, such as navigator pulses and respiratory gating, are ruled out due to the need for rapid imaging. A DCE-MRI analysis package, therefore, requires a robust image co-registration capability. Perhaps most significantly, DCE-MRI analysis requires the estimation of an arterial input function (AIF), defined as the tracer time-concentration curve in arterial plasma. This presents a problem, however, because motion corrupts the apparent MR signal, and blood is obviously in continuous motion. For this reason, it is typically not sufficient to simply observe a region in a large artery in order to estimate the AIF. Several groups have developed automated methods for calculating an accurate AIF from standard DCE-MRI data.

  These or similar methods are critical to achieving minimum measurement variability in DCE-MRI analysis. 

 Figure 1. Data showing the relationship between MR signal increase and concentration of gadolinium from a scan of a DCE-MRI phantom. This relationship should be roughly linear in this case. Data indicate a severe problem with spatially varying coil sensitivity for this system.			

DCE-MRI has historically been severely limited in its clinical utility by poor reproducibility even in untreated subjects over very short time frames, with scan–rescan CVs ranging from 18%–25%.

  However, recent studies undertaken by VirtualScopics in conjunction with MediciNova (San Diego, CA) and others have demonstrated that CVs of 7%–10% are achievable with a properly designed protocol, strict quality controls on the imaging site equipment, and a well-conceived analysis system that addresses the major issues outlined earlier.    

The central dilemma faced by an investigator who wishes to incorporate quantitative imaging into a clinical trial is this:  Current radiological practice is geared toward subjective interpretation rather than objective quantification. Figure 2 shows a uniformity and linearity phantom scan obtained during study initiation QA of an MRI system that was in clinical use and had passed standard QA procedures. The obvious distortion seen in this scan was not noted by the site radiologists because that level of distortion is simply not relevant to subjective interpretation. However, if the goal of an imaging session is to obtain precise structural measurements, distortion of this sort is completely unacceptable. Moreover, imaging site technologists and radiologists are often not familiar with the image acquisition techniques that are necessary for obtaining quantitative biomarkers, and very few site radiologists are in possession of or trained in using the analysis software packages necessary for extracting biomarkers like cartilage volume, plaque burden, or blood flow from image data. Finally, it is vital in a multisite study to ensure that the quality and consistency of data obtained from the various sites are comparable despite the differences that may exist among sites in scanner type and locally available expertise. 

Figure 2. MRI scan of a volume and linearity phantom obtained using a well-maintained system (left). MRI scan of the same phantom from a clinical scanner that was in routine use at the site (right).			

All of these considerations point to the necessity of applying centralized control to the image acquisition and analysis process for clinical trials if reliable quantitative results are to be obtained. Such control can be applied in a wide variety of ways—by a CRO, an imaging core lab or even an expert group within the sponsor's organization—but it must be applied early in the process and consistently throughout the trial. Specifically, it is necessary to have an imaging protocol designed with the particular requirements of the desired imaging biomarkers in mind. It is necessary to perform a thorough QA check on the scanning systems at all imaging sites, with the understanding that it is not possible to rely on the site's own QA procedures, and to review scanner quality periodically throughout the course of the trial. It is also necessary to provide thorough training to the site technologists and radiologists in the particulars of the study protocol and to ensure that each site is able to implement the designed protocol precisely. Finally, it is necessary to perform the analysis in a robust and consistent way, automating as many aspects as possible to reduce the possibility of measurement variability and human error. 

Introducing an imaging component to a clinical trial without sufficient planning and attention to detail will generally result in a tremendous amount of wasted time and money. However, quantitative imaging has the potential—by improving the researcher's ability to assess efficacy early in the development process—to crack many of the bottlenecks that currently make drug development such a time-consuming and expensive endeavor. 

 1. P. Therasse, S. Arbuck et al., "New Guidelines to Evaluate the Response to Treatment in Solid Tumors," 

 2. R. Twombly, "Criticism of Tumor Response Criteria Raises Trial Design Questions," 

 3. R.S. Benjamin, H. Choi et al., "Response of Gastrointestinal Stromal Tumors (GISTs) to Imatinib by Choi Criteria and Response Evaluation Criteria in Solid Tumors (RECIST) as Surrogates for Survival and Time to Progression," 2006 ASCO Annual Meeting Proceedings Part I, 9506 (2006). 

 4. P. Tofts, "Modeling Tracer Kinetics in Dynamic Gd-DTPA MR Imaging," 

 5. G. Liu, H. Rugo et al., "Dynamic Contrast-Enhanced Magnetic Resonance Imaging as a Pharmacodynamic Measure of Response After Acute Dosing of AG-013736, an Oral Angiogenesis Inhibitor, in Patients with Advanced Solid Tumors: Results from a Phase I Study," 

 6. F. Eckstein, F. Cicuttini et al., "Magnetic Resonance Imaging (MRI) of Articular Cartilage in Knee Osteoarthritis (OA): Morphological Assessment," 

 7. J. Tamez-Pena, M. Barbu-McInnis, S. Totterman, "Knee Cartilage Extraction and Bone-Cartilage Interface Analysis from 3D MRI Data Sets," 

 8. A. Bashir, M.L. Gray et al., "Nondestructive Imaging of Human Cartilage Glycosminoglycan Concentration by MRI," 

 9. D. Burstein, J.H. Velyvis et al., "Protocol Issues for Delayed Gd(DTPA)2- Enhanced MR Imaging (dGEMRIC) for Clinical Evaluation of Cartilage," 

10. E. Ashton, T. McShane, J. Evelhoch, "Inter-Operator Variability in Perfusion Assessment of Tumors in MRI Using Automated AIF Detection," 

11. M. Rijpkema, J. Kaanders et al., "Method for Quantitative Mapping of Dynamic MRI Contrast Agent Enhancement in Human Tumors," 

12. K. Murase, K. Kikuchi et al., "Determination of Arterial Input Function Using Fuzzy Clustering for Quantification of Cerebral Blood Flow with Dynamic Susceptibility Contrast-Enhanced MR Imaging," 

13. S. Galbraith, M. Lodge et al., "Reproducibility of Dynamic Contrast-Enhanced MRI in Human Muscle and Tumours:  Comparison of Quantitative and Semi-Quantitative Analysis," 

 is chief scientific officer with VirtualScopics, Inc., 350 Linden Oaks, Rochester, NY 14580, email: 

The technique offers a clear window into drug effects and provides strong safety and efficacy evidence.

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